# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================

"""Adapted from rllab maze_env.py."""

import random
import os
import tempfile
import xml.etree.ElementTree as ET
import math
import numpy as np
import gym

from hbaselines.envs.efficient_hrl import maze_env_utils
from hbaselines.envs.efficient_hrl.humanoid import HumanoidEnv

# Directory that contains mujoco xml files.
SCRIPT_PATH = os.path.abspath(os.path.dirname(__file__))
MODEL_DIR = os.path.join(SCRIPT_PATH, 'assets')


class HumanoidMazeEnv(gym.Env):
    """Gym representation of HumanoidMaze, HumanoidPush, or HumanoidFall."""

    MODEL_CLASS = HumanoidEnv
    MAZE_HEIGHT = None
    MAZE_SIZE_SCALING = None

    def __init__(self,
                 maze_id=None,
                 maze_height=0.5,
                 maze_size_scaling=4,
                 n_bins=0,
                 sensor_range=3.,
                 sensor_span=2 * math.pi,
                 observe_blocks=False,
                 put_spin_near_agent=False,
                 top_down_view=False,
                 image_size=64,
                 manual_collision=False,
                 *args,
                 **kwargs):
        """Instantiate the environment.

        Parameters
        ----------
        maze_id : str
            the type of maze being simulated. Can be 'Maze', 'Push', 'Fall', or
            'Block'.
        maze_height : float, optional
            height of the floor
        maze_size_scaling : float, optional
            scaling factor for the maze. Specifies the size of one block.
        n_bins : int, optional
            number of vieable objects
        sensor_range : float, optional
            distance whereby objects can be perceived. Must be within the span
            as well.
        sensor_span : float, optional
            degrees of visibility
        observe_blocks : bool, optional
            whether to add the movable blocks to the observations
        put_spin_near_agent : bool, optional
            specifies whether the agent can spin blocks
        top_down_view : bool, optional
            if set to True, the top-down view is provided via the observations
        image_size: int
            determines the width and height of the rendered image
        manual_collision : bool, optional
            if set to True, collisions cause the agent to return to its prior
            position
        """
        self._maze_id = maze_id
        self.t = 0

        model_cls = self.__class__.MODEL_CLASS
        if model_cls is None:
            raise AssertionError("MODEL_CLASS unspecified!")
        xml_path = os.path.join(MODEL_DIR, model_cls.FILE)
        tree = ET.parse(xml_path)
        worldbody = tree.find(".//worldbody")

        self.MAZE_HEIGHT = height = maze_height
        self.MAZE_SIZE_SCALING = size_scaling = maze_size_scaling
        self._n_bins = n_bins
        self._sensor_range = sensor_range * size_scaling
        self._sensor_span = sensor_span
        self._observe_blocks = observe_blocks
        self._put_spin_near_agent = put_spin_near_agent
        self._top_down_view = top_down_view
        self._manual_collision = manual_collision

        self.MAZE_STRUCTURE = structure = maze_env_utils.construct_maze(
            maze_id=self._maze_id)
        # Elevate the maze to allow for falling.
        self.elevated = any(-1 in row for row in structure)
        # Are there any movable blocks?
        self.blocks = any(
            any(maze_env_utils.can_move(r) for r in row)
            for row in structure)

        torso_x, torso_y = self._find_robot()
        self._init_torso_x = torso_x
        self._init_torso_y = torso_y
        self._init_positions = [
            (x - torso_x, y - torso_y)
            for x, y in self._find_all_robots()]

        self._xy_to_rowcol = lambda x, y: (
            2 + (y + size_scaling / 2) / size_scaling,
            2 + (x + size_scaling / 2) / size_scaling)
        # walls (immovable), chasms (fall), movable blocks
        self.image_size = image_size

        height_offset = 0.
        if self.elevated:
            # Increase initial z-pos of humanoid.
            height_offset = height * size_scaling
            torso = tree.find(".//body[@name='torso']")
            torso.set('pos', '0 0 %.2f' % (1.4 + height_offset))
        if self.blocks:
            # If there are movable blocks, change simulation settings to
            # perform better contact detection.
            default = tree.find(".//default")
            default.find('.//geom').set('solimp', '.995 .995 .01')

        self.movable_blocks = []
        for i in range(len(structure)):
            for j in range(len(structure[0])):
                struct = structure[i][j]
                if struct == 'r' and self._put_spin_near_agent:
                    struct = maze_env_utils.Move.SpinXY
                if self.elevated and struct not in [-1]:
                    # Create elevated platform.
                    ET.SubElement(
                        worldbody, "geom",
                        name="elevated_%d_%d" % (i, j),
                        pos="%f %f %f" % (j * size_scaling - torso_x,
                                          i * size_scaling - torso_y,
                                          height / 2 * size_scaling),
                        size="%f %f %f" % (0.5 * size_scaling,
                                           0.5 * size_scaling,
                                           height / 2 * size_scaling),
                        type="box",
                        material="",
                        contype="1",
                        conaffinity="1",
                        rgba="0.9 0.9 0.9 1",
                    )
                if struct == 1:  # Unmovable block.
                    # Offset all coordinates so that robot starts at the
                    # origin.
                    ET.SubElement(
                        worldbody, "geom",
                        name="block_%d_%d" % (i, j),
                        pos="%f %f %f" % (j * size_scaling - torso_x,
                                          i * size_scaling - torso_y,
                                          height_offset +
                                          height / 2 * size_scaling),
                        size="%f %f %f" % (0.5 * size_scaling,
                                           0.5 * size_scaling,
                                           height / 2 * size_scaling),
                        type="box",
                        material="",
                        contype="1",
                        conaffinity="1",
                        rgba="{} {} 0.4 1".format(i / len(structure),
                                                  j / len(structure[0])),
                    )
                elif maze_env_utils.can_move(struct):  # Movable block.
                    # The "falling" blocks are shrunk slightly and increased in
                    # mass to ensure that it can fall easily through a gap in
                    # the platform blocks.
                    name = "movable_%d_%d" % (i, j)
                    self.movable_blocks.append((name, struct))
                    falling = maze_env_utils.can_move_z(struct)
                    spinning = maze_env_utils.can_spin(struct)
                    x_offset = 0.25 * size_scaling if spinning else 0.0
                    y_offset = 0.0
                    shrink = 0.1 if spinning else 0.99 if falling else 1.0
                    height_shrink = 0.1 if spinning else 1.0
                    movable_body = ET.SubElement(
                        worldbody, "body",
                        name=name,
                        pos="%f %f %f" % (
                            j * size_scaling - torso_x + x_offset,
                            i * size_scaling - torso_y + y_offset,
                            height_offset +
                            height / 2 * size_scaling * height_shrink),
                    )
                    ET.SubElement(
                        movable_body, "geom",
                        name="block_%d_%d" % (i, j),
                        pos="0 0 0",
                        size="%f %f %f" % (
                            0.5 * size_scaling * shrink,
                            0.5 * size_scaling * shrink,
                            height / 2 * size_scaling * height_shrink),
                        type="box",
                        material="",
                        mass="0.001" if falling else "0.0002",
                        contype="1",
                        conaffinity="1",
                        rgba="0.9 0.1 0.1 1"
                    )
                    if maze_env_utils.can_move_x(struct):
                        ET.SubElement(
                            movable_body, "joint",
                            armature="0",
                            axis="1 0 0",
                            damping="0.0",
                            limited="true" if falling else "false",
                            range="%f %f" % (-size_scaling, size_scaling),
                            margin="0.01",
                            name="movable_x_%d_%d" % (i, j),
                            pos="0 0 0",
                            type="slide"
                        )
                    if maze_env_utils.can_move_y(struct):
                        ET.SubElement(
                            movable_body, "joint",
                            armature="0",
                            axis="0 1 0",
                            damping="0.0",
                            limited="true" if falling else "false",
                            range="%f %f" % (-size_scaling, size_scaling),
                            margin="0.01",
                            name="movable_y_%d_%d" % (i, j),
                            pos="0 0 0",
                            type="slide"
                        )
                    if maze_env_utils.can_move_z(struct):
                        ET.SubElement(
                            movable_body, "joint",
                            armature="0",
                            axis="0 0 1",
                            damping="0.0",
                            limited="true",
                            range="%f 0" % (-height_offset),
                            margin="0.01",
                            name="movable_z_%d_%d" % (i, j),
                            pos="0 0 0",
                            type="slide"
                        )
                    if maze_env_utils.can_spin(struct):
                        ET.SubElement(
                            movable_body, "joint",
                            armature="0",
                            axis="0 0 1",
                            damping="0.0",
                            limited="false",
                            name="spinable_%d_%d" % (i, j),
                            pos="0 0 0",
                            type="ball"
                        )

        torso = tree.find(".//body[@name='torso']")
        geoms = torso.findall(".//geom")
        for geom in geoms:
            if 'name' not in geom.attrib:
                raise Exception("Every geom of the torso must have a name "
                                "defined")

        _, file_path = tempfile.mkstemp(text=True, suffix='.xml')
        tree.write(file_path)

        self.wrapped_env = model_cls(*args, file_path=file_path, **kwargs)

    def get_ori(self):
        """Return the orientation of the humanoid."""
        return self.wrapped_env.get_ori()

    def set_goal(self, goal):
        """Set the goal position of the humanoid."""
        self.wrapped_env.set_goal(goal)

    def get_range_sensor_obs(self):
        """Return egocentric range sensor observations of maze."""
        robot_x, robot_y, robot_z = self.wrapped_env.get_body_com("torso")[:3]
        ori = self.get_ori()

        structure = self.MAZE_STRUCTURE
        size_scaling = self.MAZE_SIZE_SCALING
        height = self.MAZE_HEIGHT

        segments = []
        # Get line segments (corresponding to outer boundary) of each immovable
        # block or drop-off.
        for i in range(len(structure)):
            for j in range(len(structure[0])):
                if structure[i][j] in [1, -1]:  # There's a wall or drop-off.
                    cx = j * size_scaling - self._init_torso_x
                    cy = i * size_scaling - self._init_torso_y
                    x1 = cx - 0.5 * size_scaling
                    x2 = cx + 0.5 * size_scaling
                    y1 = cy - 0.5 * size_scaling
                    y2 = cy + 0.5 * size_scaling
                    struct_segments = [
                        ((x1, y1), (x2, y1)),
                        ((x2, y1), (x2, y2)),
                        ((x2, y2), (x1, y2)),
                        ((x1, y2), (x1, y1)),
                    ]
                    for seg in struct_segments:
                        segments.append(dict(
                            segment=seg,
                            type=structure[i][j],
                        ))
        # Get line segments (corresponding to outer boundary) of each movable
        # block within the agent's z-view.
        for block_name, block_type in self.movable_blocks:
            block_x, block_y, block_z = self.wrapped_env.get_body_com(
                block_name)[:3]
            # Block in view.
            if block_z + height * size_scaling / 2 \
                    >= robot_z >= block_z - height * size_scaling / 2:
                x1 = block_x - 0.5 * size_scaling
                x2 = block_x + 0.5 * size_scaling
                y1 = block_y - 0.5 * size_scaling
                y2 = block_y + 0.5 * size_scaling
                struct_segments = [
                    ((x1, y1), (x2, y1)),
                    ((x2, y1), (x2, y2)),
                    ((x2, y2), (x1, y2)),
                    ((x1, y2), (x1, y1)),
                ]
                for seg in struct_segments:
                    segments.append(dict(
                        segment=seg,
                        type=block_type,
                    ))

        # 3 for wall, drop-off, block
        sensor_readings = np.zeros((self._n_bins, 3))
        for ray_idx in range(self._n_bins):
            ray_ori = (ori - self._sensor_span * 0.5 +
                       (2 * ray_idx + 1.0) / (
                                   2 * self._n_bins) * self._sensor_span)
            ray_segments = []
            # Get all segments that intersect with ray.
            for seg in segments:
                p = maze_env_utils.ray_segment_intersect(
                    ray=((robot_x, robot_y), ray_ori),
                    segment=seg["segment"])
                if p is not None:
                    ray_segments.append(dict(
                        segment=seg["segment"],
                        type=seg["type"],
                        ray_ori=ray_ori,
                        distance=maze_env_utils.point_distance(
                            p, (robot_x, robot_y)),
                    ))
            if len(ray_segments) > 0:
                # Find out which segment is intersected first.
                first_seg = sorted(ray_segments, key=lambda x: x["distance"])[
                    0]
                seg_type = first_seg["type"]
                idx = (0 if seg_type == 1 else  # Wall.
                       1 if seg_type == -1 else  # Drop-off.
                       2 if maze_env_utils.can_move(seg_type) else  # Block.
                       None)
                if first_seg["distance"] <= self._sensor_range:
                    sensor_readings[ray_idx][idx] = \
                        (self._sensor_range - first_seg["distance"]) \
                        / self._sensor_range

        return sensor_readings

    def _get_obs(self):
        """Return the current step observation."""
        wrapped_obs = self.wrapped_env._get_obs()
        if self._top_down_view:
            img = self.render(mode='rgb_array',
                              width=self.image_size,
                              height=self.image_size)
            view = [(img.astype(np.float32) / 255.0).flat]
        else:
            view = []

        if self._observe_blocks:
            additional_obs = []
            for block_name, block_type in self.movable_blocks:
                additional_obs.append(
                    self.wrapped_env.get_body_com(block_name))
            wrapped_obs = np.concatenate([wrapped_obs[:3]] + additional_obs +
                                         [wrapped_obs[3:]])

        range_sensor_obs = self.get_range_sensor_obs()
        return np.concatenate(view +
                              [wrapped_obs,
                               range_sensor_obs.flat,
                               [self.t * 0.001]])

    def reset(self):
        """Reset the environment."""
        self.t = 0
        self.wrapped_env.reset()
        if len(self._init_positions) > 1:
            xy = random.choice(self._init_positions)
            self.wrapped_env.set_xy(xy)
        return self._get_obs()

    @property
    def viewer(self):
        """Return the mujoco viewer object."""
        return self.wrapped_env.viewer

    def render(self, *args, **kwargs):
        """Render the environment."""
        return self.wrapped_env.render(*args, **kwargs)

    @property
    def observation_space(self):
        """Return the properties of the state space."""
        shape = self._get_obs().shape
        high = np.inf * np.ones(shape)
        low = -high
        return gym.spaces.Box(low, high)

    @property
    def action_space(self):
        """Return the properties of the action space."""
        return self.wrapped_env.action_space

    def _find_robot(self):
        """Check that there is an agent in the current maze structure."""
        structure = self.MAZE_STRUCTURE
        size_scaling = self.MAZE_SIZE_SCALING
        for i in range(len(structure)):
            for j in range(len(structure[0])):
                if structure[i][j] == 'r':
                    return j * size_scaling, i * size_scaling
        assert False, 'No robot in maze specification.'

    def _find_all_robots(self):
        """Return the starting position of all agents.

        Returns
        -------
        list of (float, float)
            coordinates that the agents are expected to start at
        """
        structure = self.MAZE_STRUCTURE
        size_scaling = self.MAZE_SIZE_SCALING
        coords = []
        for i in range(len(structure)):
            for j in range(len(structure[0])):
                if structure[i][j] == 'r':
                    coords.append((j * size_scaling, i * size_scaling))
        return coords

    def _is_in_collision(self, pos):
        """Check whether the agent is in a collision location.

        Parameters
        ----------
        pos : (float, float)
            (x,y) position of the agent

        Returns
        -------
        bool
            True if collided, False otherwise
        """
        x, y = pos
        structure = self.MAZE_STRUCTURE
        size_scaling = self.MAZE_SIZE_SCALING
        for i in range(len(structure)):
            for j in range(len(structure[0])):
                if structure[i][j] == 1:
                    minx = j * size_scaling - size_scaling * \
                        0.5 - self._init_torso_x
                    maxx = j * size_scaling + size_scaling * \
                        0.5 - self._init_torso_x
                    miny = i * size_scaling - size_scaling * \
                        0.5 - self._init_torso_y
                    maxy = i * size_scaling + size_scaling * \
                        0.5 - self._init_torso_y
                    if minx <= x <= maxx and miny <= y <= maxy:
                        return True
        return False

    def step(self, action):
        """Advance the simulation by one step.

        In this process, if any agent is in a collision position, its x,y
        values are returned to their previous step values.

        Parameters
        ----------
        action : array_like
            actions to be performed by the agent

        Returns
        -------
        array_like
            next observation
        float
            environmental reward
        bool
            done mask
        dict
            extra information dictionary
        """
        self.wrapped_env.update_viewer()
        self.t += 1
        if self._manual_collision:
            old_pos = self.wrapped_env.get_xy()
            inner_next_obs, inner_reward, done, info = self.wrapped_env.step(
                action)
            new_pos = self.wrapped_env.get_xy()
            if self._is_in_collision(new_pos):
                self.wrapped_env.set_xy(old_pos)
        else:
            inner_next_obs, inner_reward, done, info = self.wrapped_env.step(
                action)
        # change to ignore when the agent falls
        return self._get_obs(), inner_reward, done, info
